Estimating the Probability of Default for No-Default and Low-Default Portfolios

Posted: 5 Nov 2018 Last revised: 2 Dec 2019

Multiple version iconThere are 2 versions of this paper

Date Written: May 20, 2019

Abstract

This article proposes a sequential Bayesian updating approach to estimate default probabilities on rating grade level for no- and low-default portfolios. Bayesian sequential updating allows to obtain default probabilities also for those rating grades for which no defaults have been observed. The advantage of the proposed approach is that it preserves the rank order of rating grades in case of no defaults. Rank-preservation is not ensured when using an identical prior distribution across all rating grades. We discuss Bayesian sequential updating for the beta-binomial model and a model incorporating the asymptotic single risk factor model of the Basel Accord. Practical aspects such as incorporating information from external sources and the margin of conservatism are addressed.

Keywords: No-Default Portfolio, Low-Default Portfolio, Credit Rating, Probability of Default, Basel Accord, IFRS 9, CECL

JEL Classification: G21, G24, G28

Suggested Citation

Blümke, Oliver, Estimating the Probability of Default for No-Default and Low-Default Portfolios (May 20, 2019). Available at SSRN: https://ssrn.com/abstract=3266284 or http://dx.doi.org/10.2139/ssrn.3266284

Oliver Blümke (Contact Author)

Raiffeisen Bank International ( email )

Am Stadtpark 9
Vienna, A-1030
Austria

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